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Related Concept Videos

Experimental Designs01:16

Experimental Designs

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An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
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Body:Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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Group Design02:01

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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Body:Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to...
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Natural selection, a fundamental concept in evolutionary biology, is the mechanism by which evolution is driven, favoring organisms that are best adapted to their environments. This process enhances their chances of survival and reproduction. Adaptation, a key outcome of this process, involves genetic modifications that optimize an organism's functionality under specific environmental challenges, such as extreme cold or thinner air at high altitudes.
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Dosage Regimens: Designs and Approaches01:28

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Designing a dosage regimen, which refers to the manner of drug administration, is a complex process involving the selection of drug dose, route, and frequency. This process is underpinned by pharmacokinetic parameters derived from tests and population averages. These parameters are then tailored to patient-specific variables such as diagnosis, demographics, and allergy status. Once therapy commences, therapeutic response monitoring is critical and achieved through clinical and physical...
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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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Optimized adaptive enrichment designs.

Thomas Ondra1, Sebastian Jobjörnsson2, Robert A Beckman3

  • 11 Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.

Statistical Methods in Medical Research
|December 20, 2017
PubMed
Summary
This summary is machine-generated.

Optimizing clinical trial designs for targeted therapies using Bayesian decision theory enhances expected utilities for both sponsors and society. Adaptive partial enrichment designs offer greater robustness and efficiency compared to traditional methods.

Keywords:
Adaptive designenrichment designoptimal designprecision medicinesubgroup analysis

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacoeconomics

Background:

  • Targeted therapies require efficient clinical trial designs that consider both overall and pre-defined subgroups.
  • Stakeholder preferences (commercial sponsor, society) necessitate quantifiable utility functions for decision-making.

Purpose of the Study:

  • To optimize frequentist single- and adaptive two-stage clinical trial designs for targeted therapies using a Bayesian decision theoretic approach.
  • To compare the performance of partial enrichment designs against non-enriched designs from both commercial and societal perspectives.

Main Methods:

  • Bayesian decision theory and utility functions to quantify stakeholder preferences.
  • Optimization of single-stage and adaptive two-stage designs with partial enrichment.
  • Dynamic programming for deriving optimal adaptation rules in adaptive designs.

Main Results:

  • Partial enrichment designs significantly improve expected utilities compared to non-enriched designs.
  • Adaptive partial enrichment designs demonstrate superior robustness and maintain high expected utilities under varying prior assumptions.
  • Sponsor-optimized trials yield smaller sample sizes than society-optimized trials.

Conclusions:

  • Partial enrichment strategies, particularly adaptive ones, offer substantial improvements in clinical trial efficiency and expected utility for targeted therapy development.
  • Adaptive designs provide robustness against prior misspecification, ensuring reliable outcomes.
  • Balancing commercial and societal objectives in trial design is crucial for resource optimization and maximizing health benefits.